To derive risk models for percutaneous coronary intervention (PCI) outcomes from clinical
and laboratory variables available before the procedure so they can be used for preprocedure
PATIENTS AND METHODS
Using the Mayo Clinic registry, we analyzed 9035 PCIs on 7640 unique patients from
January 1, 2000, through April 30, 2005. We included only the first PCI per patient
(n=7457). Logistic regression was used to model the calculated risk score and major
procedural complications. Separate risk models were made for mortality and major adverse
cardiovascular events (MACE) derived solely from baseline and laboratory characteristics.
Final risk scores for procedural death, defined as any death during the index hospitalization,
and MACE contained the same 7 variables (age, myocardial infarction ≤24 hours, preprocedural
shock, serum creatinine level, left ventricular ejection fraction, congestive heart
failure, and peripheral artery disease).
Models had adequate goodness of fit, and areas under the receiver operating characteristic
curve were 0.74 and 0.89 for MACE and procedural death, respectively, indicating excellent
overall discrimination. The model was robust across many subgroups, including those
undergoing elective PCI, those having diabetes mellitus, and elderly patients. Bootstrap
analysis indicated that the model was not overfit to the available data set.
Before coronary angiography is performed, a risk-scoring system based on 7 variables
can be used conveniently to predict cardiovascular complications after PCI. This model
may be useful for providing patients with individualized, evidence-based estimates
of procedural risk as part of the informed consent process.